Generalizability of REDUCE-IT eligibility criteria in a large diabetes cardiovascular outcomes trial: A post hoc subgroup analysis of EMPA-REG outcome
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Bibliographic record
Abstract
REDUCE-IT showed that icosapent ethyl (IPE) improved cardiovascular (CV) outcomes in participants with established CV disease (CVD) or type 2 diabetes (T2D) and at least one additional risk factor plus mild-moderate hypertriglyceridemia and reasonably controlled low-density lipoprotein cholesterol (LDL-C). As the generalizability of REDUCE-IT has not been investigated in a T2D population with established CVD, this post hoc analysis investigated how many participants from EMPA-REG OUTCOME, which tested the effects of empagliflozin versus placebo on CV outcomes in participants with T2D and CVD, would have been eligible for IPE treatment, and whether CV outcomes differed based on eligibility for IPE treatment. Participants from EMPA-REG OUTCOME were screened for inclusion using both REDUCE-IT-like criteria (baseline statin therapy, triglycerides 135–499 mg/dL and LDL-C 41–100 mg/dL) and slightly amended FDA indication criteria (triglycerides ≥150 mg/dL). Analyses were conducted to characterize the study population and CV outcomes in participants eligible for IPE versus those not eligible for IPE. Of the 7020 participants from EMPA-REG OUTCOME, 1810 (25.8%) fulfilled REDUCE-IT criteria and 3182 (45.3%) fulfilled FDA criteria for IPE treatment. Treatment effects of empagliflozin versus placebo on CV and kidney outcomes and mortality were consistent in participants meeting REDUCE-IT and FDA criteria and those who did not. These results indicate that a sizable proportion of patients with diabetes and established CVD, such as those in EMPA-REG OUTCOME, may be eligible for IPE treatment to lower residual CV risk. Treatment benefit with empagliflozin was consistent, regardless of REDUCE-IT or FDA eligibility criteria.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it